Until recently, graphics were typically drawn by hand to represent mathematical, statistical, and geometric relations. Computer graphics programs, particularly scientific and mathematical plotting packages, have made this task much easier but they have not altered its ad hoc aspect. Nor have statistical and mathematical packages that generate more complex graphics contributed to our understanding of how they are created.
A need exists for a computer method and system to be able to construct graphics systematically in order to handle more complex multivariate environments. This is also the case in connection with data mining computer systems. Unfortunately, the sophistication of data mining systems far exceeds the computer graphical methods used in their displays. Most data mining computer systems still rely on pie, line, and bar charts of slices of data cubes (multi-way aggregations of a subset of a database). These charts fail to reveal the relationships among the entities they represent because they have no deep grammar for generating them. They are simply hard-wired to facets of the data cube. For example, if one drills through the cube to view a different slice of the data, only a simple pie chart is obtained. A similar hard-wiring exists in displays from tree classifiers, neural networks, and other algorithms.
A need also exists for a method and apparatus for creating aesthetic graphics from data using graph algebra.